Multi-Variate Time Series Forecasting on Variable Subsets
Abstract
We formulate a new inference task in the domain of multivariate time series forecasting (MTSF), called Variable Subset Forecast (VSF), where only a subset of the variables are available during inference. Variables are absent during inference because of intermittent data collection issues (eg. sensor failures) or domain shift between train / test. To the best of our knowledge, robustness of MSTF models in presence of such failures, has not been studied in the literature.
Through extensive evaluation, we first show that the performance of state of the art methods significantly degrade in this setting.
We propose a non-parametric, wrapper technique that can be applied on top any existing forecast models. Through thorough experiments across 4 datasets and 5 forecast models, we show that our technique is able to recover the close to 95\% performance of the underlying models even when only 15\% of the original variables are present.
Through extensive evaluation, we first show that the performance of state of the art methods significantly degrade in this setting.
We propose a non-parametric, wrapper technique that can be applied on top any existing forecast models. Through thorough experiments across 4 datasets and 5 forecast models, we show that our technique is able to recover the close to 95\% performance of the underlying models even when only 15\% of the original variables are present.
Research Areas
